Assessment 1 Brief

MATH 70076 - Data Science

Dr Zak Varty

Project Brief

What are you expected to do?

“For this assessment you will submit a piece of reproducible data journalism. This will be published in a new magazine, to be launched as a competitor to New Scientist, and should follow the style guidelines of The Economist.

Your piece should be submitted as a file named YOURCID-submission.pdf and may take one of two forms:

a single, full page (20x24cm) infographic;

an article of 200-300 words with 2-3 supporting data visualisations.”

What are New Scientist articles like?

Present new findings and technical topics to a popular audience.
Think interested final year high school student in a relevant field.
Stories on topics which spark curiosity, encourage critical thinking and explore the societal implications of new findings.

What do you mean by a full page infographic?

A single, complex data visualisation (or series of simpler visualisations combined into a single graphic) with a clear narrative flow, which might be provided by text or annotations within the graphic.

Examples

What do you mean by article and supporting visualisations?

200-300 words + 2-3 supporting visualisations.
Examples from a recent Economist magazine.
More complicated examples (typically, detailed maps) might be better suited to infographic style.

Formatting

Note 1: Submitted articles should be clearly presented but final typesetting and arrangement will be controlled by the magazine’s editorial board. To ensure high print quality, all figures should be provided as either vector graphics or with a resolution of least 300 dpi.

What to write about?

Note 2: Your submitted article or infographic should be based on a data set published on data is plural archive between 2023-08-02 and 2023-12-06. Each student must use a distinct data set; you should register your your CID against your chosen dataset in this spreadsheet.

Advice for Picking a Data Set

Data quantity and quality vary, pick something that will let you show off your skills but is manageable in the time available.
Welcome to change your mind, that’s why I picked 70 data sets. Just update spreadsheet in case someone else wants your current data.
Think about how to tell a compelling story, this is easiest if the topic interests you.

Examples from last year - Article & Visualisations

Examples from last year - Full Page Infographic

Examples from last year - Somewhere In-Between

What do I submit?

Files to submit

You should submit two files in total for this assessment.

These files should be submitted via the Imperial College VLE on Blackboard by 13:00 on Wednesday 21 Feb 2024. Note that large files can take quite some time to upload. These files should be named:

YOURCID-math70076-assessment-1.pdf

YOURCID-math70076-assessment-1.zip

Please ensure that you upload each document to the correct part of the learning space. Allowances will not be made for incorrect submission.

Project Files: `YOURCID-math70076-assessment-1.zip`

This is a zipped folder containing the code, data, documentation, and anything else needed for the magazine team to reproduce your submission.

This project folder should contain a file named notes-to-editor.md. This file should contain a reflective summary of how you have designed your submission and applied the style guide, addressing each of the following points in ∼ 100 words (300 words total):

Summarise the style guidelines that are relevant to your submission;

Clearly state which guidelines are and are not met;

Describe any alterations you would make to your submission with the assistance of the magazine’s team of experienced data journalists.

Assessment Criteria

This assessment will be graded out of 20 marks in each of the following categories:

Submitted infographic or article;

Reflective summary of style guide;

Project-level structure and documentation;

Code-level structure and documentation;

Reproducibility and accuracy of overall workflow (incl. data gathering and manipulation).

Grading

The number of marks allocated in each category will be guided by the following criteria:

Mark	Description
0	Missing or highly flawed
4	Substandard: major revisions required before approval
8	Acceptable: correct up to small errors, moderate revisions required
12	Good: requires only minor revisions before approval
16	Excellent: could be approved as presented
20	Exceptional: exceeds expectations or extends taught material